Clinical and Experimental Neuroscience (NiCE), Institute for Aging Research, Biomedical Institute for Bio-Health Research of Murcia (IMIB-Arrixaca), School of Medicine, University of Murcia, Campus Mare Nostrum, 30120 Murcia, Spain.
Automation, Electrical Engineering and Electronic Technology Department, Industrial Engineering Technical School, Technical University of Cartagena, 30202 Cartagena, Spain.
Sensors (Basel). 2024 Mar 21;24(6):2015. doi: 10.3390/s24062015.
The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lays the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.
预期寿命的增加以及随之而来的老年人口的增长，是保障老年人充分健康和社会关怀的主要挑战。所提出的系统旨在提供一种工具，实现步态和平衡的自动评估，这对于预防老年人跌倒至关重要。通过 RGB-D 摄像机，可以捕获和数字化表示某些描述用户执行特定人体运动和姿势的参数。这些个体运动和姿势实际上与许多著名的步态和平衡评估测试中包含的项目相关。根据这些信息，治疗师在执行练习时无需在场，即可评估这些测试的结果，并通过存储和分析所开发系统提供的序列来做出诊断。该系统在实验室环境中进行了验证，随后在一家疗养院对六名居民进行了试用。结果表明，该系统具有实用性，并且可以通过使用从 RGB-D 传感器获取的信息计算出的参数来客观评估临床测试的主要项目，这种方式非常便捷。此外，它为创建基于云的远程跌倒风险评估平台及其与移动助理机器人的集成奠定了未来基础，还为设计能够检测模式和识别病理的人工智能模型奠定了未来基础，从而使治疗师能够预防处于风险中的用户跌倒。
